ligera_f: LIGERA: LIght GEnetic Robust Association main function
In OchoaLab/ligera: LIght GEnetic Robust Association

ligera_f

R Documentation

LIGERA: LIght GEnetic Robust Association main function

Description

This function performs the genetic association tests on every locus of a genotype matrix against a quantitative trait, given a precomputed kinship matrix. The function returns a tibble containing association statistics and several intermediates. This version calculates p-values using an F-test, which gives calibrated statistics under both quantitative and binary traits. Compared to ligera(), which uses the faster Wald test (calibrated for quantitative but not binary traits), this F-test version is quite a bit slower, and is optimized for m >> n, so it is a work in progress.

Usage

ligera_f(
  X,
  trait,
  kinship,
  kinship_inv = NULL,
  covar = NULL,
  loci_on_cols = FALSE,
  mem_factor = 0.7,
  mem_lim = NA,
  m_chunk_max = 1000,
  V = 0,
  tol = 1e-15,
  maxIter = 1e+06
)

Arguments

`X`	The `m`-by-`n` genotype matrix, containing dosage values in (0, 1, 2, NA) for the reference allele at each locus.
`trait`	The length-`n` trait vector, which may be real valued and contain missing values.
`kinship`	The `n`-by-`n` kinship matrix, estimated by other methods (i.e. the `popkin` package).
`kinship_inv`	The optional matrix inverse of the kinship matrix. Setting this parameter is not recommended, as internally a conjugate gradient method (`\link[cPCG]{cgsolve}`) is used to implicitly invert this matrix, which is much faster. However, for very large numbers of traits without missingness and the same kinship matrix, inverting once might be faster.
`covar`	An optional `n`-by-`K` matrix of `K` covariates, aligned with the individuals.
`loci_on_cols`	If `TRUE`, `X` has loci on columns and individuals on rows; if false (the default), loci are on rows and individuals on columns. If `X` is a BEDMatrix object, `loci_on_cols = TRUE` is set automatically.
`mem_factor`	Proportion of available memory to use loading and processing genotypes. Ignored if `mem_lim` is not `NA`.
`mem_lim`	Memory limit in GB, used to break up genotype data into chunks for very large datasets. Note memory usage is somewhat underestimated and is not controlled strictly. Default in Linux and Windows is `mem_factor` times the free system memory, otherwise it is 1GB (OSX and other systems).
`m_chunk_max`	Sets the maximum number of loci to process at the time. Actual number of loci loaded may be lower if memory is limiting.
`V`	Algorithm version (0, 1, 2). Experimental features, not worth explaining.
`tol`	Tolerance value passed to `\link[cPCG]{cgsolve}`.
`maxIter`	Maximum number of iterations passed to `\link[cPCG]{cgsolve}`.

Details

Suppose there are n individuals and m loci.

Value

A tibble containing the following association statistics

pval: The p-value of the association test
beta: The estimated effect size coefficient for the trait vector at this locus
f_stat: The F statistic
df: degrees of freedom: number of non-missing individuals minus number of parameters of full model

Examples

# Construct toy data
# genotype matrix
X <- matrix(
    c(0, 1, 2,
      1, 0, 1,
      1, 0, 2),
    nrow = 3,
    byrow = TRUE
)
trait <- 1 : 3
kinship <- diag( 3 ) / 2 # unstructured case

tib <- ligera_f( X, trait, kinship )
tib

OchoaLab/ligera documentation built on Jan. 5, 2023, 8:29 p.m.